Visualizing Disproportionality Indices by County for Covid-19 Data

All data is from the CDC website: https://data.cdc.gov/w/k8wy-p9cg/tdwk-ruhb?cur=voq8guDL7SP

Data is reported as of: 06/30/2021

This notebook uses the downloadable CSV version of the data (input file = 'Covid1.csv')

import pandas as pd
import numpy

df1 = pd.read_csv("Covid1.csv")
#Examine input file
df1.head(10)

	Data as of	Start Date	End Date	State	County Name	Urban Rural Code	FIPS State	FIPS County	FIPS Code	Indicator	...	COVID-19 Deaths	Non-Hispanic White	Non-Hispanic Black	Non-Hispanic American Indian or Alaska Native	Non-Hispanic Asian	Non-Hispanic Native Hawaiian or Other Pacific Islander	Hispanic	Other	Urban Rural Description	Footnote
0	6/23/21	1/1/20	6/19/21	AK	Anchorage Municipality	3	2	20	2020	Distribution of all-cause deaths (%)	...	204	0.581	0.046	0.210	0.058	0.027	0.031	0.046	Medium metro	NaN
1	6/23/21	1/1/20	6/19/21	AK	Anchorage Municipality	3	2	20	2020	Distribution of COVID-19 deaths (%)	...	204	0.324	NaN	0.324	0.147	0.093	NaN	NaN	Medium metro	One or more data cells have counts between 1-9...
2	6/23/21	1/1/20	6/19/21	AK	Anchorage Municipality	3	2	20	2020	Distribution of population (%)	...	204	0.556	0.052	0.081	0.095	0.026	0.091	0.099	Medium metro	NaN
3	6/23/21	1/1/20	6/19/21	AL	Baldwin County	4	1	3	1003	Distribution of all-cause deaths (%)	...	255	0.906	0.076	NaN	NaN	NaN	0.012	NaN	Small metro	One or more data cells have counts between 1-9...
4	6/23/21	1/1/20	6/19/21	AL	Baldwin County	4	1	3	1003	Distribution of COVID-19 deaths (%)	...	255	0.863	0.110	NaN	NaN	NaN	NaN	NaN	Small metro	One or more data cells have counts between 1-9...
5	6/23/21	1/1/20	6/19/21	AL	Baldwin County	4	1	3	1003	Distribution of population (%)	...	255	0.832	0.086	0.007	0.011	0.001	0.047	0.017	Small metro	NaN
6	6/23/21	1/1/20	6/19/21	AL	Calhoun County	4	1	15	1015	Distribution of all-cause deaths (%)	...	302	0.814	0.171	NaN	NaN	NaN	0.009	NaN	Small metro	One or more data cells have counts between 1-9...
7	6/23/21	1/1/20	6/19/21	AL	Calhoun County	4	1	15	1015	Distribution of COVID-19 deaths (%)	...	302	0.788	0.199	NaN	NaN	NaN	NaN	NaN	Small metro	One or more data cells have counts between 1-9...
8	6/23/21	1/1/20	6/19/21	AL	Calhoun County	4	1	15	1015	Distribution of population (%)	...	302	0.716	0.210	0.004	0.009	0.001	0.041	0.019	Small metro	NaN
9	6/23/21	1/1/20	6/19/21	AL	Colbert County	4	1	33	1033	Distribution of all-cause deaths (%)	...	175	0.822	0.166	NaN	NaN	NaN	0.009	NaN	Small metro	One or more data cells have counts between 1-9...

10 rows × 21 columns

# Calculate disproportionality indices for different population sub-groups

#Non-HS-White
column1 = df1.iloc[1::3,12]
column2 = df1.iloc[2::3,12]

vals1 = column1.values/column2.values
numpy.nan_to_num(vals1,False,0)
codes = df1.iloc[2::3,8]
states = df1.iloc[2::3,3]
counties = df1.iloc[2::3,4]

#Non-HS-Black
column3 = df1.iloc[1::3,13]
column4 = df1.iloc[2::3,13]
vals2 = column3.values/column4.values
numpy.nan_to_num(vals2,False,0)



#Non-HS-Asian
column5 = df1.iloc[1::3,15]
column6 = df1.iloc[2::3,15]
vals3 = column5.values/column6.values
numpy.nan_to_num(vals3,False,0)


#HS
column7 = df1.iloc[1::3,17]
column8 = df1.iloc[2::3,17]
vals4 = column7.values/column8.values
numpy.nan_to_num(vals4,False,0)


data = {'FIPS': codes, 'State': states, 'County Name':counties, 'NonHSWhite':vals1, 'NonHSBlack':vals2,'NonHSAsian':vals3,'HS':vals4}
df2 = pd.DataFrame(data)
df2.to_csv('output3.csv')


#Examine output file
df2.head(10)

	FIPS	State	County Name	NonHSWhite	NonHSBlack	NonHSAsian	HS
2	2020	AK	Anchorage Municipality	0.582734	0.000000	1.547368	0.000000
5	1003	AL	Baldwin County	1.037260	1.279070	0.000000	0.000000
8	1015	AL	Calhoun County	1.100559	0.947619	0.000000	0.000000
11	1033	AL	Colbert County	1.015444	1.037500	0.000000	0.000000
14	1039	AL	Covington County	0.993983	1.330645	0.000000	0.000000
17	1043	AL	Cullman County	1.061135	0.000000	0.000000	0.000000
20	1047	AL	Dallas County	1.125926	0.977240	0.000000	0.000000
23	1049	AL	DeKalb County	1.208020	0.000000	0.000000	0.000000
26	1055	AL	Etowah County	1.091731	0.948387	0.000000	0.000000
29	1069	AL	Houston County	1.051515	1.025830	0.000000	0.617647

1.1 Choropleth map of Disproportionality Indices by county of Covid-19 deaths - Non-Hispanic White:

token = "pk.eyJ1Ijoic3MtMTIzIiwiYSI6ImNraXl4MjU1eTB5ajkyd21vNGlzMW9ucnYifQ.pg8uh9S1bMrPuKlWKsPNlQ" # you will need your own token

from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties = json.load(response)

df = pd.read_csv("output3.csv")

import plotly.graph_objects as go


fig = go.Figure(go.Choroplethmapbox(geojson=counties, locations=df.FIPS, z=df.NonHSWhite,
                                    colorscale="Viridis", zmin=0, zmax=3, marker_line_width=0))

fig.update_layout(mapbox_style="light", mapbox_accesstoken=token,
                  mapbox_zoom=3, mapbox_center = {"lat": 37.0902, "lon": -95.7129})
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

1.2 Choropleth map of Disproportionality Indices by county of Covid-19 deaths - Non-Hispanic Black:

fig = go.Figure(go.Choroplethmapbox(geojson=counties, locations=df.FIPS, z=df.NonHSBlack,
                                    colorscale="Plotly3", zmin=0, zmax=3, marker_line_width=0))

fig.update_layout(mapbox_style="light", mapbox_accesstoken=token,
                  mapbox_zoom=3, mapbox_center = {"lat": 37.0902, "lon": -95.7129})
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

1.3 Choropleth map of Disproportionality Indices by county of Covid-19 deaths - Non-Hispanic Asian:

fig = go.Figure(go.Choroplethmapbox(geojson=counties, locations=df.FIPS, z=df.NonHSAsian,
                                    colorscale="OrRd", zmin=0, zmax=3, marker_line_width=0))

fig.update_layout(mapbox_style="light", mapbox_accesstoken=token,
                  mapbox_zoom=3, mapbox_center = {"lat": 37.0902, "lon": -95.7129})
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

1.4 Choropleth map of Disproportionality Indices by county of Covid-19 deaths - Hispanic:

fig = go.Figure(go.Choroplethmapbox(geojson=counties, locations=df.FIPS, z=df.HS,
                                    colorscale="Turbo", zmin=0, zmax=3, marker_line_width=0))

fig.update_layout(mapbox_style="light", mapbox_accesstoken=token,
                  mapbox_zoom=3, mapbox_center = {"lat": 37.0902, "lon": -95.7129})
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

Comparing Race/Ethnicity data

import plotly.express as px
fig = px.bar(df,x=df.State,y=[df.NonHSWhite,df.NonHSBlack, df.NonHSAsian,df.HS])
fig.update_xaxes(type='category')
fig.show()

fig = px.scatter(df, x=df.State, y=df.NonHSWhite,color = df.NonHSWhite,
                 size=df.NonHSWhite)
fig.show()

fig = px.scatter(df, x=df.State, y=df.NonHSBlack,color = df.NonHSBlack,
                 size=df.NonHSBlack)
fig.show()

fig = px.scatter(df, x=df.State, y=df.NonHSAsian,color = df.NonHSAsian,
                 size=df.NonHSAsian)
fig.show()

fig = px.scatter(df, x=df.State, y=df.HS,color = df.HS,
                 size=df.HS)
fig.show()

Resources:

For more information on healthcare disparities, healthy equity and outcomes, please visit the following links:

https://www.cdc.gov/coronavirus/2019-ncov/community/health-equity/racial-ethnic-disparities/index.html

https://www.cdc.gov/coronavirus/2019-ncov/community/health-equity/race-ethnicity.html

https://jamanetwork.com/journals/jama/fullarticle/2775687